In a synchronous hypergraph, a node is denoted by a nonterminal with a bispan.

a node in the source hypergraph is a nonterminal that spans a continuous sequence of words of source sentence.


Here, G denotes all potential synchronous grammars.

the size of potential SCFGs G is extremely large given a vocabulary Ω, resulting in a large number of hyperedges in source hypergraph.


The approximation makes the training algorithm tractable.

there is still one problem: how to efficiently construct the synchronous hypergraph?


The number of these rules is comparable with that of grammar extracted by the traditional pipeline, which has 13.2 millions rules.

the two grammars are quite different as shown in Table 1.


The improvement of UDSGI over Baseline is statistically significant (Koehn, 2004)  We can not directly evaluate the quality of grammar, since there is not a golden grammar.

as grammar is used to generate target translations, it's reasonable to decide the quality of a grammar by testing what best translations it can produce.


The baseline still extracts rules from the 200K data, while our approach also learns grammar on 200K data.


In this way, they prefer word alignments that are consistent with syntactic structure alignments.

labeled word alignment data are required in order to learn the discriminative model.


There is a significant literature in sentence compression aimed at modeling the first of these, length: producing meaning-preserving alternations that reduce the length of the input string (Chandrasekar et al., 1996;Vanderwende et al., 2007;Clarke and Lapata, 2008;Cohn and Lapata, 2009;Yatskar et al., 2010).

we know of no previous work aimed at modeling meaning-preserving transformations that systematically transform the register or style of an input string.


Presumably this is due to the conflation of stylistic differences and semantic adequacy discussed in §3.

it also appears that the correlation between BLEU and human style judgments is too low to be of practical use for evaluating style.


Under an intension based perspective, a learner's primary goal is to understand word meanings, and it is the similarities between words'intensions that cause word choice confusions.

this is not to say that learners ignore word usages.


Using RCA, the system would consider all three to be mutually confusable because they appear almost equally frequently in the same context.

while the preposition selector considers p b and p c to be confusable with p a , it does not conclude that p b and p c are also mutually confusable under context C. Thus, if most usage contexts contain only one or two preposition types, the preposition selector and RCA may produce similar confusion sets; but if the data also include usage contexts that contain three or more preposition types, RCA may offer confusion sets based on a more globally optimized similarity metric.


The scores of baseline method 2 were zero for three programs, whose summaries did not share words with that of the target program.

proposed method 2 properly scored these programs.


The method presented in (Li et al., 2010) is slightly similar with our work for it considered novelty, coverage and balance wholly.

sentence features of existing literatures were usually acquired asynchronously.


We used the JUMAN/KNP 1 analyzer to parse sentences and obtain the structures automatically.

not every P-A pair is meaningful in information navigation; actually, only a fraction of the patterns are useful.


Even though word ordering errors in Chinese sentences may result in incorrect segmentations, the experiments show that the word-based approach (i.e., with segmentation) is better than the character-based approach (i.e., without segmentation) irrespective of the use of different segmentation systems and reference corpora.

although the Chinese Web POS 5-gram corpus is smaller than the Google Chinese Web 5-gram corpus, the experiments show that they have similar performances on the WOE detection.


Machine translation aims to generate a target sentence that is semantically equivalent to the source sentence.

most of current statistical machine translation models do not model the semantics of sentences.


Virtually, in order to project the translation candidates of source elements to target-side-like PAS, we require that a source argument only aligns to a target argument.

the result of bilingual SRL usually does not satisfy this requirement.


Intuitively, the combination can be operated directly by cube pruning (Chiang, 2007).

since the source elements are translated independently and many source elements' spans are very short, numerous phrase translation rules are ignored during translation.


Therefore, the number of big PASs used in PAS(BTG) reduces.

the child PAS in PAS+BTG is only a choice but not essential for translating its father PAS.


Furthermore, in the second example, our PASbased method successfully recognizes the [AM-TMP] argument "2005年" and move it to the end of sentence.

the BTG system only performs translation without any reordering.


Parse trees are indispensable to the existing tree-based translation models.

there exist two major challenges in utilizing parse trees: 1) For most language pairs, it is hard to get parse trees due to the lack of syntactic resources for training.


The parse tree is usually generated by a linguistic parser which is trained on a manually annotated corpus, such as Treebank.

the manually annotated corpus is always too inadequate to fully display the strengths of tree-based models.


Theoretically, in a sub-sentence pair, all included words cannot align to words outside it.

since many words are wrongly aligned via the automatic word alignment, numerous correct aligned sub-sentence pairs are often excluded under this restriction.


Bilingual sentence segmentation leads to a great space reduction for constructing packed forests.

even after sentence segmentation, the generation space of tree structures would be still very large, especially when the sub-sentence is very long.


The reason is that as a sequence labeling model, CRF gives a most probable abbreviation character sequence by analyzing local information for each character.

for Chinese abbreviations, local information alone is not adequate.


The reason is that CRF+AEPW tries to extract information between well-formed full forms and also well-formed abbreviations.

in the current Chinese abbreviation generation process, some ill-formed candidates may be generated, like include illegal terms and common phrases which are in fact substrings of the full form.


They also reported their results on CTB5.1 data set.

we didn't make a comparison as they employed a different conversion method for CS-to-DS.


These knowledge bases have been shown to form a valuable component for many natural language processing tasks such as knowledge base population , text classification (Wang and Domeniconi, 2008), and cross-document coreference (Finin et al., 2009).

to be able to utilize or enrich these KB resources, the applications usually require linking the mentions of entities in text to their corresponding entries in the knowledge bases, which is called entity linking task and has been proposed and studied in Text Analysis Conference (TAC) since 2009 (McNamee and Dang, 2009).


Given the query, the previous systems are static at evaluation time and their training does not depend on the input query.

figure 2 illustrates the distribution of the labeled instances related to the three names in a feature space (Bag of Words, Named Entities and Edit Distance, the popular features used in previous work).


Thus, in this benchmark, the set of company names in the training and test corpora are different.

the lazy learning approach proposed in this paper demonstrates that it is feasible to train separate system for each company, and the system can immediately react to any company name without manually labeling new corpora.


However, for some queried names, it is hard to find a sufficient number of unambiguous synonyms or the related documents containing these synonyms.

the total number of available manually labeled instances M for other irrelevant names is relatively large.


Then, we decompose the weight vector u l for problem l into two parts: one part that models the distribution knowledge specific to each problem l and one part that models the common predictive structure, where w l and v l are weight vectors specific to each prediction problem l. Then, the parameters Θ, w l and v l can be learned by joint empirical risk minimization, i.e., by minimizing the joint empirical loss of the predictors for the K problems on the training instances as Eq.

5, It shows that w l and v l are estimated on n (l) training instances of problem l. Θ is estimated on all the training instances of the K problems.


As previous entity linking systems do not consider the information of the queried name, they usually use all the instances in M without any difference to train the linker.

in data set M, some instances related with some particular names may share more predictive information with the queried name than other instances.


Traditional approaches usually need an additional classification step to resolve this problem (Zheng et al., 2010;Lehmann et al., 2010).

our approach seamlessly takes into account the NIL prediction problem.


Hence, comparing with previous work, it increases the response time of the system for each query.

many of the entity linking applications such as knowledge base population do not require real-time user interaction, and therefore they are time-insensitive applications.


, it is obvious that the term "charge" is the direct neighbour of term "capacitor", hence, the dependency relation path length d r_path_l en equals to 1 as shown in Figure 1(b) (1).

"charge" is a bit farther away from the term "farad" as the d r_path_l en between term "charge" and "farad" equals to 2 as shown in Figure 1(b) (3).


(Ming et al., 2010) utilized three domain specific metrics to explore term weights and integrated them into existing IR models.

most of these term weighting based retrieval models ignore the dependency relations between term pairs.


In previous work on morphological learning, such as (Chan, 2008), (Zhao and Marcus, 2011) and (Goldwater et al., 2006(Goldwater et al., , 2011, the straight lines on logistic scales are usually interpreted as following Zipf's law (Zipf, 1949).

lognormal distributions with large variance also yield straight lines on both the log-log rank-frequency plot and the CCDF plot.


The rule-based bootstrapping algorithm utilizes type frequencies only, no matter what form of input is given.

as shown in (Goldwater et al., 2006), when Dirichlet-multinomial model is assumed, the option of utilizing token frequency in generative model doesn't help, and in the contrast it hurts the inference of the generative model.


For example, as shown in Section 3.1, the bootstrapping algorithm measures contextual diversity with type frequency only.

natural text data typically use most types of words more than once.


We have shown the acquisition outputs of the bootstrapping algorithm in Table 1, upon which we can build a rule-based segmentation model.

to the learning progress of generative models, which will converge to a relatively steady state, we stop the acquisition process after 20 bootstrapping iterations following previous experiments.


Search log sessions contain a large number of paraphrases contributed by users during query rewriting.

it is a big challenge to distinguish paraphrases from the simply related queries in the sessions.


On the one hand, related queries can be used for query suggestion and recommendation, using which the users can extend their search interest and get some related information.

the related queries are not suitable for direct query rewriting with the purpose of retrieving more and better results exactly reflecting the user's requirement, since the related queries often have different meanings from the original user query.


what we learn are paraphrase patterns rather than related queries or patterns.


The third class, i.e., spelling correction was not conventionally regarded as a type of paraphrase.

pattern pairs of this class actually convey the same requirement of users and are useful in applications such as query correction.


Question retrieval in CQA archives aims to find historical questions that are semantically equivalent or relevant to the queried questions.

question retrieval is challenging partly due to the word ambiguity and lexical gap between the queried questions and the historical questions in the archives.


Therefore, it is a meaningful task to retrieve the semantically equivalent or relevant questions to the queried questions.


(2010) enriched the original word-based representation with a concept-based representation, thereby proposing the translation of the original word language to a concept language.

their translation models are based solely on the use of translation at the lexical level (e.g., word-to-concept), and thus their method is very different from our context-dependent style of translation.


Na and Ng (2011) also applied automatic translation for monolingual retrieval.

they used the expected frequency of a word computed from all possible translated representations, while we use the state-of-the-art commercial machine translation service (e.g., Google Tranlate), which is much simpler than their translation strategies.


Moreover, these studies performed translation without taking into account the context information of an original word (Chen and Gey, 2004;Kraaij et al., 2003).

our approach is contextdependent and thus produces different translated words depending on the context of a word in original language.


In dependency and CCG parsing, shift-reduce parsing is among the best-performing algorithms (Huang and Sagae, 2010;Zhang and Clark, 2011).

compared to commonly-used statistical parsers available on the web such as Charniak-Johnson (Charniak and Johnson, 2005) and Petrov-Klein (Petrov and Klein, 2007), shift-reduce constituency parsers still have room left for further improvements on parsing accuracy.


that, on Chinese parsing our parser outperforms Bikel (2004) and Charniak (2000) by 0.6% and 0.4%, respectively.

our parser lags behind Petrov and Klein (2007) and the reranking parser (Charniak and Johnson, 2005).


Supervised computational systems can be trained to mark up the AZ structure of a text automatically (see Section 2); the output of such systems has been shown to aid summarisation and human browsing of the scientific literature (Teufel and Moens, 2002;Guo et al., 2011a;Contractor et al., 2012).

supervised systems require manually annotated training data that must be created anew for each discipline (and language) before they can be deployed, while large quantities of unannotated text are often available.


Moreover, to solve this issue using a supervised learner, one needs the gold standard of coreference at least on the target side of the bitext.

given such data, the typological differences in languages can be exploited to aid a CR system to perform better than if CR is performed independently for each language.


The alignment described in the previous section is sufficiently accurate for content words, such as verbs, nouns, and adjectives.

errors become more frequent as we move to pronouns.


It shows that more than 55% of English personal pronouns are dropped from the surface representation of the Czech sentence, though still present in its deep structure.

english pleonastic pronouns are not present even there.


The CR system we use definitely does not aim to compete with current state-of-the-art systems.

for the purpose of research on crosslingual CR, it can be employed as a reasonable baseline.


Related studies have focused on detecting noteworthiness from meeting transcripts (Banerjee and Rudnicky, 2009).

very little work has been done to date to identify this kind of information in other types of human communication, such as spontaneous phone conversations.


The advent of online social networks has produced a crescent interest on the task of sentiment analysis for short text messages (Go et al., 2009;Barbosa and Feng, 2010;Nakov et al., 2013).

sentiment analysis of short texts such as single sentences and and microblogging posts, like Twitter messages, is challenging because of the limited amount of contextual data in this type of text.


In our experiments we focus in sentiment prediction of complete sentences.

we show the impact of training with sentences and phrases instead of only sentences.


In our experiments, we check whether using examples that are single phrases, in addition to complete sentences, can provide useful information for training the proposed NN.

in our experiments the test set always includes only complete sentences.


Previous work in NLP on sentiment analysis has mainly focused on explicit sentiments.

as noted in , many opinions are expressed implicitly, as shown by this example: Ex(1) The reform would lower health care costs, which would be a tremendous positive change across the entire health-care system.


There is an explicit positive sentiment toward the event of "reform lower costs".

in expressing this sentiment, the writer also implies he is negative toward the "costs", since he's happy to see the costs being decreased.


Different from their work, which do not cover all cases relevant to gfbf events,  defines a generalized set of implicature rules and proposes a graph-based model to achieve sentiment propagation between the agents and themes of gfbf events.

that system requires all of the gfbf information (Q1)-(Q4) to be input from the manual annotations; the only ambiguity it resolves is sentiments toward entities.


the writer has opposite sentiments toward entities in a bf relation.


That is, the system may know businesses in Santa Fe, but not whether they sell roasted chiles.

collectively people themselves know this type of information, and they frequently mention it in social media.


The recently proposed methods, such as CBOW and Skip-gram, have demonstrated their effectiveness in learning word embeddings based on context information such that the obtained word embeddings can capture both semantic and syntactic relationships between words.

it is quite challenging to produce high-quality word representations for rare or unknown words due to their insufficient context information.


Suitable initialization will help increase the embedding quality which works like training with multi-epochs.

as there are two matrix M and M in our network structure, the initialization of both of them are more sensible.


Some recent studies attempted to train multi-prototype word embeddings through clustering context window features of the word.

due to a large number of parameters to train, these methods yield limited scalability and are inefficient to be trained with big data.


Compared with traditional single prototype model, these models have demonstrated significant improvements in many semantic natural language processing (NLP) tasks.

they suffer from a crucial restriction in terms of scalability when facing exploding training text corpus, mainly due to the deep layers and huge amounts of parameters in the neural networks in these models.


Compared with conventional neural network language models which usually set up a multi-layer neural network, Word2Vec merely leverages a threelayer neural network to learn word embeddings, resulting in greatly decreased number of parameters and largely increased scalability.

similar to most of existing word embedding models, Word2Vec also assumes one embedding for one word.


We also conduct a comparison on the number of parameters between the new EM algorithm and the stateof-the-art multi-prototype model proposed in (Huang et al., 2012), which can illustrate the efficiency superior of our algorithm.

to the conventional ways of using context words to predict the next word or the central word, the Skip-Gram model (Mikolov et al., 2013b) aims to leverage the central word to predict its context words.


Now imagine that in the corpus used to train the parser none of these nouns have been observed, then it is unlikely that these attachments can be resolved correctly.

if an accurate noun-adjective bilexical operator were available most of the uncertainty could be resolved.


The advantage of this approach is that as long as we can estimate the distribution of contexts of words we can compute the value of the bilexical operator.

this approach has a clear limitation: to design a bilinear operator for a target linguistic relation we must design the appropriate distributional representation.


Traditionally, grammar-based methods have been used for CSL, but more recently machine learning approaches to semantic structure computation have been shown to yield higher accuracy.

most previous work did not exploit syntactic/semantic structures of the utterances, and the state-of-the-art is represented by conditional models for sequence labeling, such as Conditional Random Fields (Lafferty et al., 2001) trained with simple morphological and lexical features.


Hashtags have been proven to be useful for many applications, including microblog retrieval (Efron, 2010), query expansion (A. Bandyopadhyay et al., 2011), sentiment analysis (Davidov et al., 2010;Wang et al., 2011).

only a few microblogs contain hashtags provided by their authors.


Our motivation to test multiple classifiers stemmed also from related works which mostly test more than one classifier.

the choice between state-of-the-art linear classifiers might not be much of importance, as the most important is the feature engineering.


Czech is a highly flective language and uses a lot of diacritics.

some Czech users type only the unaccented characters.


We adapted all their preprocessing pipelines.

as the number of combinations would be too large, we report only the settings with better performance.


occurrence > 5) + extended pointedness; FS2: POS word-shape + pattern + POS characteristics + emoticons + word-case.

imbalanced distribution data in the real world do not necessarily resemble the balanced distribution.


Thus, it means that learning from different views should be softly regularized towards a common latent structure.

it is not easy to directly formulate it in a probabilistic framework, because weak consensus modeling can not be separated from a joint higher task, i.e., recovering spare rating matrix, in our case.


The generative process is described as follows: , and set the item latent vector as -draw the response , univariate Gauss distribution, where c ij is a confidence parameter for rating r ij , a > b. c ij = a (higher confidence), if r ij = 1, and c ij = b, if r ij = 0.

cTR does not take the complex social network information, which is available and crucial in many real-world applications, into consideration.


In addition, we can see that CTR-smf (Purushotham et al., 2012) is sensitive to the quality of graph (SMF-1 with low quality and SMF-2 with high quality as shown in Figure 2).

we can use the low quality noisy graph (SMF-1) to improve the overall performance by this transformation process.


OmegaWiki is a freely editable online dictionary like Wiktionary.

instead of distinct language editions, OmegaWiki contains language-independent concepts ("Defined Meanings") which carry lexicalizations in different languages.


Wiktionary-Wikipedia (English): No evaluation dataset (let alone a full alignment) has been reported for this resource pair yet.

as the datasets for WordNet-Wiktionary (Meyer and  and WordNet-Wikipedia (Niemann and Gurevych, 2011) are lexically overlapping, we were able to automatically create a gold standard for Wiktionary-Wikipedia by exploiting the transitivity of the alignment relation, i.e.


We were able to create a gold standard in a novel way by exploiting the fact that many German Wiktionary senses contain links to the corresponding Wikipedia articles, inducing a sense alignment between the two LSRs manually validated by the Wiktionary community.

we were unable to extract such an alignment for English, as Wikipedia articles are attached to the lexical entry page in this version and not to a specific sense.


(Bond and Foster, 2013)) and overlap of domain labels (Wikipedia, Wiktionary, Word-Net, OmegaWiki).

for none of these features we could observe any significant 1 impact on the results, mostly due to sparsity of the respective features.


For conversion from PS to DS, a head-table approach (Magerman, 1994;Collins, 2003;Yamada and Matsumoto, 2003;Sun and Jurafsky, 2004;Nivre, 2006;Johansson and Nugues, 2007;Duan et al., 2007;Zhang and Clark, 2008) is widely used.

the reliability of head tables has been questioned (Xue, 2007).


Secondly, we detect and annotate clause pairs in a document that hold logical discourse relations.

since this is too complicated to assign as one task using crowdsourcing, we divide the task into two steps: determining the existence of logical discourse relations and annotating the type of relation.


For the examples in Table 5, we confirmed that the discourse relation types of the top four examples were surely correct.

we judged the type (Contrast) of the bottom example as incorrect.


On the one hand, using words as the reordering units will reduce the number of candidates generated.

word segmentation results will affect the performance of WOE detection and correction.


If a segment without WOEs is misjudged to be erroneous, the word order still has a chance to be kept by the WOE correction models.

if a segment with WOEs is misjudged to be correct, words in the misjudged segment will not be reordered in the correction part because the error correction module is not triggered.


Clearly, the recall at the segment level and the correctable rate at the sentence level are 1 by the all-tag-B baseline.

its accuracy at the segment and the sentence levels are low.


It means the correct candidates are ranked 3.7 on the average.

the MRR by using the C sys dataset is 0.208.


We address the task of automatic terminology acquisition (ATA), the task of finding technical terms in texts without reliance on existing resources that list terms of the domain.

to this stands automatic terminology recognition (ATR), which we define as finding known terms and their variants (Jacquemin and Bourigault, 2003).


This is similar to distant supervision (Mintz et al., 2009) which also uses pre-existing resources such as gazetteers for, e.g., relation extraction.

our method is applied to ATA for the technological domain and does not rely on precompiled resources -we make use of figure references, which are an inherent part of patents.


Our method can be characterized as training data identification: we exploit given conditions in patents for our search of training data.

training data recognition methods need precompiled resources as input and search for instances of resource elements in texts.


Like our first baseline, it needs no training data.

to our first baseline, it was specifically designed for terminology acquisition.


We would also like to evaluate candidate classification on gold boundaries (manually verified boundaries of term candidates); this allows us to quantify by how much performance can be improved if candidate identification is perfect.

since gold boundary annotation is expensive, we instead approximated it: (i) We run automatic term candidate identification.


All observations hold for T l dev and T l test .

numbers are higher for T l dev because the ratio of FRTCs to candidates is higher than for T l test (38% vs. 27%) which improves classification performance on T l dev -this holds for ATAS as well as for the baselines.


In such cases the figure reference serves as a disambiguator.

in other positions they are non-terms, e.g., "They include braces, collars, splints and other similar apparatus".


We consider that selecting a value of n grater than 4 could lead to find few n-grams, so that many web pages could be under-represented.

previous experiments using also bigrams showed that they are not suitable for this approach.


According to the model, one of the most prominent distinctions is that amateurs tend to refer to the opponent as he, whereas experts use white and black more frequently.

it is of course not universally true, which leads to the misclassification of some experts as amateurs.


This seems counterintuitive at first, as we may expect lower-rated players to be less familiar with such terms.

it appears that they are frequently overused by weaker players.


(2003) presented the theories and approaches to calculate the frequencies of Tibetan characters, pieces, syllables and words based on a large scale Tibetan corpus including about 40, 000, 000 syllables (Lu et al., 2003).

a large part of the corpus they used are Buddhist literatures and the work can't be done well without a pragmatic Tibetan word segmentation tool (Chen et al., 2003a;Chen et al., 2003b;Jiang, 2006;Jiang and Kong, 2006;Sun et al., 2009;Sun et al., 2010;Lu and Shi, 2011;Liu et al., 2012a).


The sources and scales in different units are shown in  It's a heavy task to manually classify those document into domains.

we still can get the domain information for a certain subsets of the corpus.


maturity, background of information etc."

we do not know of any such investigations for Bangla text readability that have investigate the way background of a reader affect the readability of text.


This group of models moves beyond the surface features of a text and try to measure objectively the different cognitive indicators associated with text and the reader.

it has been observed that, many situations, some traditional indicators perform as well as the newer and more difficult versions (Crossley et al., 2007).


For adult data, it can be seen that this feature has a strong and significant correlation, which not true for the user data of group 2 for separate texts.

for the common texts this feature was found to have high significant correlation with both the reader groups.


the correlations with $(noun phrase), $(verb phrase) $(postpositions), #(postpositions), #(adjective) were found to be insignificant.


Different with normal practice in WSI work, there is no feature engineering in our model.

our BNP model outperformed all the systems on supervised evaluation.


Those previous works on combining TM and SMT can be classified into four categories: (1) selecting the better translation sentence from TM and SMT (He et al., 2010a;2010b;Dara et al., 2013); (2) incorporating TM matched sub-segments into SMT in a pipelined manner (Koehn and Senellart, 2010;; (3) only enhancing the SMT phrase table with new TM phrase-pairs (Biç ici and Dymetman, 2008;Simard and Isabelle, 2009); and (4) incorporating the associated TM information with each source phrase to guide the SMT decoding .

all previous works mentioned above only focus on the case in which the TM database and the SMT training set share the same data-set.


Therefore, the factor ( | ) from the test set will possess a different probability distribution in comparison with that from the training set.

the development set is not big enough (only a few hundreds sentence-pairs at each interval) to re-train all TM factors of the proposed model.


For example, at interval [0.9, 1.0), those added TM phrase-pairs significantly improve the SMT system from 63.65 to 73.55, and Model-III from 80.69 to 86.40.

if Model-III + is compared with Model-III, the improvements from merging the TM phrase-pairs get less when the fuzzy match score decreases, because the matched TM parts are fewer at low fuzzy match intervals.


However, it is still worse than the TM in TER (13.32 vs. 10.42).

although Model-III has greatly exceeded the SMT at each interval, Model-III + still significantly outperforms Model-III at most intervals.


In real environments, the SMT training set and the TM database could be the same before translation projects starts.

the TM database will gradually deviate from the SMT training set while the translation task progresses.


The useful reordering pattern learned through this example is: Ich kann umstellen → I can rearrange which is memorized through the operation sequence: Generate(Ich, I) -Generate(kann, can) -Insert Gap -Generate(umstellen, rearrange) It can generalize to the test sentence shown in Figure 1(a).

it fails to generalize to the sentences in Figure 1(b) and (c) although the underlying reordering pattern is the same.


All three measures showed divergence in message similarity both between individuals, and in the community as a whole, across time.

the common theme with all these techniques is that although they can effectively measure adaptation of linguistic feature use within and between dialogs, they fail to capture the precise direction of convergence or divergence between individuals (i.e., do both interactants within a conversational pair accommodate their language use to the same extent?)


The most widely used similarity measure in the field of natural language processing may be cosine similarity.

in the context of Twitter, the large scale of massive tweet data inevitably makes it expensive to perform cosine similarity computations among tremendous data samples.


For instance, 美 is likely to be an NE of Location when it refers to America.

when it expresses the sense of beautiful, it should not be an NE.


The basic claim leading most authors to neglect this kind of context is that, due to its assumed undifferentiated distribution, this information presents a challenge for classifiers to accurately use it in class membership decisions, which is bound to negatively affect results (see Cooke and Gillam (2008), Turney and Pantel (2010), Bullinaria and Levy (2012), among many others).

with this mainstream position, Rumshisky et al.


Our main claim is that devising a strategy to informatively include this type of distributional information in classification tasks can allow us to take advantage of a bigger portion of the data available in corpora and improve the accuracy of classifiers in this way.

with mainstream approaches to cue-based lexical-semantic classification, we argue for the inclusion of a type of distributional information typically not considered to be indicatory of class membership, and thus not informative to automatic classification systems.


According to the results, unmarked contexts allow us to gain an average of 10.2 points in recall for class members, demonstrating that they provide useful information to classifiers, which allows them to cover cases which they were not able to before, most likely due to phenomena such as data sparsity.

the impact on precision varies between classes, as the inclusion of very frequent information in the vectors representing target nouns may provide additional noise to the classifier (see Section 6.1).


The impact of this misleading information is made apparent by the amount of FP observed in classification results.

silence has to do with the well known problem of data sparsity, which can be caused by the particular distribution of lexical, and thus strict, though informative, contexts used in cue-based classification tasks, which are often rare in any corpus of any size due to their specificity.


An effect of a class collectively having a more heterogeneous linguistic behavior is that the evidence regarding each of its marks will typically be more disperse and, as a result, often not strong enough to be considered by classifiers, which explains the improvement introduced by unmarked contexts.

classes like HUM are composed of nouns that generally occur in a common set of prototypical contexts of that class.


Texts of Summary and Experience are unstructured information, while texts of Skills & Expertise are structured information.

some skills in the Skill & Expertise fields may not be mentioned in the Summary and Experience fields.


We show a concrete example for the two heterogeneous dependency trees in Figure 1, where six of the twelve dependencies are consistent in the two dependency trees (shown by the solid arcs).

differences between heterogeneous dependencies can possibly boost the evidences of the consistent dependencies.


, Using a beam aims to reduce complexity to O(K|A|(3n − 1)) ≈ O(n) and makes inference computationally tractable in practice.

it makes inference incomplete (the parser may fail to find a solution even if it exists) and does not guarantee the solution to be optimal.


We believe the LR framework can also shed light on theoretical, practical and experimental issues related to phrase structure parsing by comparison with dependency parsing.

using the LR automaton to constrain the parsing model for multi-word expressions turns out to be disappointing since it forces the parser to take less local decisions for which the beam approximation is not well suited.


Following the release of the PDTB, smaller corpora annotated with discourse relations have been developed for Hindi (Oza et al., 2009), Turkish (Zeyrek and Webber, 2008), Arabic (Al-Saif and Markert, 2010), and the effort is on-going with Chinese (Zhou and Xue, 2012).

for the vast majority of languages, such well-annotated resource for discourse relations is not available.


If an English connective is aligned to one of the Chinese connectives, we can transfer its label from English to the Chinese connective.

it is highly likely that a Chinese connective appears in the source sentence but the reference translation used an alternative expression or paraphrase rather than the 100 identified connectives in the PDTB.


Moreover, many studies addressed the topic diversification approach for re-ranking the retrieved results of a single query.

these approaches are not directly applicable to multiple queries.


The second one is the merging technique used by the above-mentioned Watson system (Budzik and Hammond, 2000), which uses Round robin merging, hence it is noted Round-robin.

our proposed method, DivM, is a diverse merging technique which we now proceed to define formally.


When comparing Round-robin versus SimM, the scores show the superiority of the former method when the number of conveyed topics in fragments is higher than the number of recommended documents, because it provides a diverse lists of documents in which documents relevant to less important topics are not displayed.

when the number of topics is smaller than the number of recommendations, SimM provides better results.


Implicit relations between two text spans are inferred by the reader even if they are not explicitly connected through lexical cues.

explicit relations are explicitly identified with syntactically well-defined terms, so called discourse markers or discourse connectives (DCs).


Although Versley (2010) used a list of DCs in generating the dataset, he also tried to automatically induce the DCs from his corpus.

versley (2010) did not explicitly evaluate his list of DCs, but rather focused on his parser.


In other words, by choosing a threshold for LLR, we can  label each potential DC candidate as "DC" if its LLR is above the threshold or "non-DC" otherwise.

choosing the LLR threshold depends on the application and there is no principled way to determine an ideal value for the threshold.


For example, when evaluating the candidate "à ce point", we have to label it as a wrong DC because it is not repertoried in LEXCONN.

it is a segment of the French DC "à ce point que" and only one word is missing in the expression.


(2010) suggested to consider a partial collocation as a true positive, since it signals the presence of the longer collocation.

this "was not a decision that human evaluators were comfortable with" (Kilgarriff et al., 2010).


Syntax, for example, is strongly constrained by meter.

additional features like meter and rhyme properties might be useful.


Theoretically, it would be possible to assign all songs a rating by transferring an album rating to all songs in the album.

in practice this is difficult to do robustly because album ratings and lyrics come from different sites and are not trivial to align.


The n-gram model hones in on the topic of a text, while the extended model captures more abstract structural and stylistic properties.

both perform similarly on individual genres, i.e., they both in themselves capture important aspects of 'genre'.


By inspecting the confusion tables, we discovered that old and new are separated well from each other, with new being classified as old in only 23% of the cases and the opposite happening in 17% of the cases.

mid-age shows the lowest F-Score and is misclassified in an almost symmetrical way (new: 27%, mid-age: 41%, old: 32%).


The above discourse relation labeling tasks are done on the datasets of different size for different languages at the intra-/inter-sentential levels, thus the results cannot be compared directly.

these works show a tendency: discourse connectives are useful clues for explicit discourse relation recognition, and the uses of Chinese connectives in discourse relation labeling are more challenging than those of English connectives.


The formulation of the log-likelihood ratio in Dunning (1993) is a two-tailed statistical test that if p 1 and p 2 significantly diverge from each other, the −2 log λ would get a high value.

as mentioned above, we are just interested in the cases that p 1 is much higher than p 2 , because, otherwise, coreference links among the mentions which have the relation r in common are less frequent than expected.


We also try to incorporate the question text into the above estimation formula.


Another research perspective on question answering service is quality prediction including answer quality prediction (Harper et al., 2008;Shah and Pomerantz, 2010;Severyn and Moschitti, 2012;Severyn et al., 2013) and question quality prediction (Anderson et al., 2012).

since the methods mentioned above are based on the history data, the system will experience the cold start problem.


One simple way to include phrases in topic modelling is to treat each phrase as a single term.

this method is not ideal because the meaning of a phrase is often related to its composite words.


It is because replacing many words with phrases decreases the number of co-occurrences in the corpus.

lDA(w_p) is slightly better than the other two baselines on most domains because some frequent phrases add more reliable co-occurrences in the corpus.


In contrast, LDA(w_p) is slightly better than the other two baselines on most domains because some frequent phrases add more reliable co-occurrences in the corpus.

as we point out in the introduction, some problems still exist.


Statistical tests show that our proposed method, LDA(p_GPU), outperforms all other three methods significantly (p < 0.05) using both top 15 and top 30 terms.

there's no significant improvement between any pair of the three baselines.


Then one infers that screen is an opinion target according to Assumption 1 (whether screen is correct is not checked).

in Example 2(a), we can see that good is an opinion word and it modifies thing, but thing is not related to This work is licenced under a Creative Commons Attribution 4.0 International License.


opinion words or targets) for supervision, which are regarded as positive labeled examples for classification.


At the same time, happy is a correct opinion word, so the whole expression happy day also has a small reconstruction score and then be misclassified.

the reconstruction score of happy day from OCDNN is quite large so the phrase is dropped.


Obviously, the training data constructed based on this method is not perfect.

since this method can effectively generate a great quantity of data, we think that general characteristics can be modeled with the generated training data.


The size of training data of different targets vary greatly in the dataset.

comparing with other method, the proposed method is the most stable one.


(2012) proposed to use adaptive method for this task.

most of these methods focused on the text with predefined surface words.


This is consistent with the conclusions of the work cited: if the reciprocity condition is applied too strictly, it does not improve the nearest neighbor lists over all the words.

s-norms seem better able to take advantage of the ranking.


On the one hand, mention-pair systems classify two mentions in a text as coreferent or not, by using a feature vector obtained from this pair of mentions.

entity-centric approaches determine if a mention (or a partial entity) belongs to another partial entity, using features from other mentions of the same (partial) entities.


On the one hand, the entity-centric approach allows the system to use all the features of an entity when a mention is evaluated.

the multi-pass model dynamically enriches an entity (with new features) in every iteration.


Note that if our probabilities are calculated from the full set of n-gram counts for the corpus being segmented and the set of possible segmentations S is not constrained, a segmentation with a smaller number of breaks will generally be preferred over one with more breaks.

in practice we will be greatly constraining S and also using probabilities based on only a subset of all the information in the corpus.


Unsurprisingly, the DOCREP binary representation does not compress as well as textual serialisation formats with lots of repetition, such as XML or the original stand-off annotation files.

under all of these reported situations, apart from the UIMA compressed binary format, our DOCREP representation is two to five times smaller than its UIMA counterpart, and 15 times smaller than the representation in MySQL.


We recorded both initialisation and rule application time for the two programs, via instrumentation in case of fomacg-proc and by running the grammar first on an empty file and then on the test corpus in case of cg-proc.

as initialisation is a one-time cost, in the following we are mainly concerned with the time required for applying rules.


(Clearly, for grammars with several sections, instead of a single tree that contains all rules, one tree must be built for each section to preserve rule priorities.

this does not affect the reasoning above).


It can be seen that hierarchical rule testing indeed improves performance: even a single level of merging results in 30-42% speedup.

it is also immediately evident that aside from special cases, the disadvantages overweight the benefits: memory usage and binary size grow exponentially, affecting compilation and grammar loading time as well, and very soon we run into the limits of physical memory.


The paper reports a binary size similar to the original grammar size.

as the framework breaks away from the practice of direct rule application followed in this paper and in related literature (Hulden, 2011;Peltonen, 2011), closer inspection remains as future work.


However, due to the limitation of scale and genre coverage of labeled data, it is very difficult to further improve the performance of supervised parsers.

it is very time-consuming and labor-intensive to manually construct treebanks.


Similar phenomena exist in Japanese dialogues.

most pronouns are omitted (called zeropronouns), and zero-anaphora resolution is necessary for Japanese PASA.


Namely, anaphora resolution across multiple sentences is important to dialogue analysis.

most arguments and the predicate appear in the same sentence in the accusative/dative cases of newspapers.


These methods have been shown effective on words.

the number of features is much larger than the vocabulary size, which makes it infeasible to apply them on features.


These works used manual techniques for identifying the type of support in user messages and hence, are limited to a small number of messages as compared to the real world data.

the current work builds machine learning classifiers that can automatically predict the type of support in messages.


Users with momentous activities will attract many other users to be connected with.

no body will be interested in users with trivial or insignificant behaviors.


Yu and Lam (2008) proposed an integrated probabilistic and logic approach based on Markov Logic Networks (MLNs) (Richardson and Domingos, 2006) to encyclopedia relation extraction.

this modeling only captures single relation extraction task.


(2013) presented a semi-supervised graph-based approach to joint Chinese word segmentation and POS tagging.

none of these models has been investigated or applied to social media and social network analysis.


In other words, 75% users form high density of relationship ties and the average clustering coefficient (ACC) is high (0.61).

the tie density of the remaining 25% users is much lower, since the ACC of these users is only 0.18.


The aspects "iFixit" and "repairability" refer to the unveiling of the iFixit repairability report for the Surface in February.

we also see more traditional product feature or attributes that are not correlated to external events but are key discussion points across multiple months, such as "keyboard" or "touch cover".


For example, in several papers, frequently occurring noun phrases is used as the building block for detecting aspects (Hu and Liu, 2004a;Hu and Liu, 2004b;Ku et al., 2006).

for microblogs, frequency of a noun phrase alone is an insufficient indicator of an aspect, due to the inherent noise (unlike reviews, microblog posts are short and often not as focused) and redundancy (e.g., due to retweeting in the context of Twitter).


The aspect 'lomborg', referring to Bjorn Lomborg was the subject of much discussion in March; With his article in WSJ on heavy carbon-di-oxide emissions from electric cars charging, Lomborg created a stir among environmentalists.

the top ranking aspects for Hyundai on Twitter corresponded mostly to chatter about various car models.


If a news sentence is referred or paraphrased by many tweets, it is assumed to be indicated as more important.

a tweet, besides its local importance indicator, may be more important if it is similar to the theme of the news content.


-LexRank (news) performs the second best in task 1.

the performance of LexRank (tweets) is the worst in task 2.


Intuitively, the latter is helpful if an author has a concise way of expressing herself so that the concatenated document allows to extract a statistic that is sufficient for capturing her style.

instance-based approaches are better suited for expressive authors and have advantages in sparse data scenarios.


Thus, MKL and SSAD with function words and BoW kernel confirm todays assumption that all 12 papers have been written by Madison.

choosing SSAD as the base classifier in the absence of prior knowledge leaves much room for interpretations and the user in the need of deciding between three solutions, depending on which kernel she prefers.


Our proposed method of text aesthetics prediction is similarly based on extracting characteristic features from the text passages.

in the case of literature, it is worth mentioning that in contrast to image aesthetics it is more difficult to describe the subtle attributes which differentiate an aesthetically pleasing text from its counterpart.


In general, automatic extraction system has better coverage but less accuracy compared to YAGO based system.

automatic extraction to background knowledge may help in real applications by improving coverage greatly.


In early stage, most researches rely on the similarity between the context of the mention and the definition of candidate entities by proposing different measuring criteria such as dot product, cosine similarity, KL divergence, Jaccard distance and more complicated ones (Bunescu and Pasca, 2006;Cucerzan, 2007;Zheng et al., 2010;Hoffart et al., 2011;Zhang et al., 2011).

these methods mainly rely on text similarity but neglect the internal structure between mentions.


The merit of such retrieval-based approaches is that, owing to the diversity of the web, systems can retrieve at least some responses for user input, which solves the coverage problem.

this comes at the cost of utterance quality.


Since the initial publication of this model, a number of extensions have been proposed, the majority of which are focused on enriching the original feature set.

these enriched feature sets are usually application-specific, i.e., it requires a certain expertise and intuition to conceive good features.


Moreover, for sentence ordering, combining our model with entity-based transition features achieves the best performance.

for essay scoring, the combination is detrimental.


In our re-implementation of B&L, we use the same parameter settings as B&L's original model, i.e., the optimal transition length k = 3 and the salience threshold l = 2.

when extracting entities in each sentence, e.g., dollar, yesterday, etc., we do not perform coreference resolution; rather, for better coverage, we follow the suggestion of Elsner and Charniak (2011) and extract all nouns (including non-head nouns) as entities.


We encode the RST-style discourse relations in a similar fashion to PDTB-style encoding.

since the definition of discourse roles depends on the particular discourse framework, here, we adapt Lin et al.


Because the RST-style discourse parser we use is trained on a fraction of the WSJ corpus, we remove the training texts from our dataset, to guarantee that the discourse parser will not perform exceptionally well on some particular texts.

since the PDTB-style discourse parser we use is trained on almost the entire WSJ corpus, we cannot do the same for the PDTB-style parser.


Still, on a per-question basis, for the medium and difficult questions where the pupils used the search engine, they slightly improved their performance.

the sample and the effect size are too small to draw any reliable conclusions.


Again, the result supports our prediction that usage search engines can help resolve writing uncertainties.

a deeper analysis reveals that the large effect is due to the synonym operator.


All these approaches rely on supervised training data to train the normalization model.

we use an unsupervised approach to learn the normalization lexicon of word forms in SMS to standard text.


The unsupervised normalization lexicon learning using deep learning performs a good job of learning SMS shorthands.

the induced lexicon contains only one-to-one word mappings.


In the absence of segmentation, the cached table was used for 12.8% and 14.4% of the total phrases for English-Spanish and Spanish-English, respectively.

with phrase segmentation the cached table was used for 29.2% and 39.2% of total phrases.


Both corpora were used in the Automatic Content Extraction (ACE) technology evaluation, at the coarse-grained level only.

these corpora are governed by a costly annual license, which prevents the researcher from accessing and utilising them.


When it comes to the newswire corpus, this is due to differences in the way the NE phrases are written in a newswire domain.

the boundaries of multi-word NE phrases are difficult to detect, in Arabic, due to the fact that the language has a complex morphology.


Thus far, this study has examined the window-based and dependency-based representation of evidence, in order to increase the performance of the classification process.


The goal of this experiment was to evaluate the usefulness of injecting the clustering information from Brown algorithm into the supervised model.

the actual size of the corpora mentioned in section 2.3 is too small to apply the Brown algorithm.


The state of the art performance, using distant supervision (Li et al., 2012), achieves an end-to-end Avg-Q score of 0.678 (on training data), where we achieve 0.679 (on evaluation data).

our scores are not directly comparable since we reduce the number of classes (and the amount of evidence) in our evaluation.


these three kinds of features are the most important components of Kazakh language, and they reflect the characteristic of Kazakh language.


This work put maximum entropy model into recognition of basic Kazakh phrase.

there are still space for improvement on scale and accuracy rate comparing to English and Chinese.


For instance, if cross-validation is done over all pairs in BLESS in the U+W2 300 space, Concat achieves .98 accuracy, while Diff obtains .90.

in this setting the same words appear in the training and test sets (albeit in different pairs).


For example, and will occur beside several other words like school, elephant and pipe with more or less equally distributed co-occurrence counts with all of these words.

the co-occurrence distribution of school will be skewed, with more bias towards to, high and bus than over, through and coast, with the list of words occurring beside school also being much smaller than that for and.


Data sparsity is expected to lead to low performance.

the correct analysis of compound nouns is important for a number of NLP tasks, for example in machine translation (Bouillon et al., 1992;Rackow et al., 1992;Johnston and Busa, 1999;Navigli et al., 2003).


For example, Roberts (2003) proposes that the combination of uniqueness and a presupposition of familiarity underlie all definite descriptions.

possessive definite descriptions (John's daughter) and the weak definites (the son of Queen Juliana of the Netherlands) are neither unique nor necessarily familiar to the listener before they are spoken.


However, possessive definite descriptions (John's daughter) and the weak definites (the son of Queen Juliana of the Netherlands) are neither unique nor necessarily familiar to the listener before they are spoken.

to the reductionist approaches are approaches to grammaticalization (Hopper and Traugott, 2003) in which grammar develops over time in such a way that each grammatical construction has some prototypical communicative functions, but may also have many non-prototypical communicative functions.


'He asked that, looking into her eyes' Russian verbal adverbs are similar to participial constructions in adverbial usage (or gerunds) existing in a variety of languages.

they also exhibit significant differences.


He could have asked his son/daughter to invite Mary for dinner.

the phrase I called Mary at the request of my father is still appropriate provided the act of inviting Mary contains calling her as its essential part.


Like any verb, buy and sell are supplied with subcategorization frames (aka government patterns in the Meaning -Text approach) that list all their arguments and their means of expression.

their being conversives implies that their lexical functional description should indicate the correlation between their argument structures.


When we pass from the keyword to such a derivative, we may find that an actant either stays in its initial position (teach mathematicsteacher of mathematics), or changes its number (the verb dominates the preposition -the preposition depends on the verb), or gets blocked altogether (drive home -*driver home).

the syntactic position of the actant can only change in a very limited way.


Here we will only be interested in one of these arguments -that of the whole, expressed prototypically by preposition iz 'of' as represented in phrases the majority of cases, most of the students.

in sentences with AdvD v bol'šinstve 'mostly, for the most part' this valence slot is filled, as a rule, by the subject of the dominating verb: (22) Oni byli arestovany i podverglis' v bol'šinstve svoem ssylke v Gvianu i na Sejšel'skie ostrova 'they were arrested and mostly exiled to Guiana or Seychelles' [= 'most of them were exiled…'] this is not the only possible syntactic role for this actant.


Another similarity metric that can be used is LM Perplexity.

in the current scenario we do not have resources (training data) to build a source side LM for computing the perplexity.


The main reason for this is because the development set used to train the LM is relatively small, at only 1166 sentences.

as our goal in this paper is to perform error analysis on set of data which we already have parallel references (in this case, the development set), the generalization ability of the model is not necessarily fundamental to our task at hand.


This is a natural result, as the BLEU metric puts a heavier weight on the brevity penalty assigned to shorter translations.

the RIBES-optimized LM detects more reordering errors than the BLEU-optimized LM.


Furthermore, their results are from a much bigger beam (10 times larger than their baseline), so it is not clear which factor contributes more, the larger beam size or the Slm.

our approach gains significant improvements over a state-of-theart tree-to-string baseline at a reasonable speed, about 6 times slower.


(Li and Sun, 2009;Jiang et al., 2013) utilized the massive manual natural annotations or punctuation information on the Internet to improve the performance of CWS.

these natural annotations are just partial annotations and their roles depend on the qualities of the selected resource, such as Wikipedia.


The independent classifier approach has the advantage of a simple model structure with a search space for tagging of O(|T |n).


For language processing applications, unsupervised learning of morphology can provide decentquality analyses without resources produced by human experts.

while morphological analyzers and large annotated corpora may be expensive to obtain, a small amount of linguistic expertise is more easily available.


(2013) outperforms FlatCat for Finnish and reaches the same level for English.

we show that a discriminative model such as CRF gives inconsistent segmentations that do not work as well in a practical application: In English and Finnish information retrieval tasks, FlatCat clearly outperformed the CRF-based segmentation.


In addition, case elements are basically placed in the order of a nominative, a dative and an accusative.

the basic order of case elements is often changed by being influenced from grammatical and discourse factors.


Since there are a huge number of the structures S = ⟨O, D⟩ which are theoretically possible for an input sentence B, an efficient algorithm is desired.

since O and D are dependent on each other, it is difficult to find the optimal structure efficiently.


Second, the candidate 3 is generated by the concatenating process in ConcatReorder(M 2,3 , M 4,4 ).

the reordering process in ConcatReorder(M 2,3 , M 4,4 ) generates no candidates because b 4 has no child in M 4,4 .


We can see that our method can achieve the complicated word reordering.

figure 5 shows an example of sentences incorrectly reordered and parsed by our method.


For instance, the date of Haiti earthquake considers the earthquake itself as the main event.

related events such as the sorrow expression of UN Secretary General also happen immediately after the earthquake but still in the same date.


(2012) uses a small, hand-coded grammar to describe a sparse set of prescribed activities.

we utilize corpus statistics to aid the description of a wide range of naturally-occurring videos.


However, all these conclusions came from training an SVM classifier in only one fixed topic.

in our paper, we draw our conclusions from all possible training/testing combinations rather than fixing in advance the training topic.


This cost of $90 for Doodling consists of hosting and bandwidth charges incurred for two virtual servers running on a commercial cloud platform.

once we scale Doodling up to permit more users and higher productivity, we expect the costs to remain fixed, whereas MT costs will scale proportionally to the productivity at US$0.10 per source.


The drawer scribbling answers to the canvas is a most obvious form of cheating, which may require sophisticated image recognition algorithms to weed out automatically.

we opted for a low-cost approach of allowing any guesser to mark a certain game round as cheating, if they find the drawer scribbling on the canvas.


Moreover, example (4) can be an answer to example (5).

those text pieces share neither any lexical root (make vs. ingredient) nor any syntactic structure (X predicate Y vs. predicate preposition (of) X linking-verb (is) Y) nor part-of-speech (verb vs. noun), so recognizing them as a textual entailment relation is harder than between examples (1) and (2).


Therefore, after tagging partsof-speech to every word in the glosses, regular expressions are used to capture verbs.

texts from DBpedia are composed of several sentences and contain comparatively large numbers of verbs.


We also compared the performances of PreDic to the performances of WordNet.

getting similar verbs of PreDic from WordNet was hard because WordNet does not directly provide verbs for a noun unless the noun itself also has a verb form.


Table 6 shows that PreDic is the best at recall for both micro average (i.e., 0.43) and macro average (i.e., 0.42), while NOMLEX is best at precision for both micro average (i.e., 0.77) and macro average (i.e., 0.48).

the large difference in precision for NOMLEX between micro and macro average (i.e., 29 percent) shows that NOMLEX performs well on some nouns but not on other nouns.


However, the large difference in precision for NOMLEX between micro and macro average (i.e., 29 percent) shows that NOMLEX performs well on some nouns but not on other nouns.

preDic provides not only the better recall and broader coverage than NOMLEX and WordNet, but also competitive macro average precision (i.e., 0.39 vs. 0.41) compared to NOMLEX or even better micro average precision (i.e., 0.60 vs. 0.52) compared to WordNet.


Many researchers have suggested effective approaches for verb entailment acquisition and built valuable lexical resources with which the variability of natural language expression can be understood more systematically.

unsupervised verb inference from nouns that can deliver similar meaning without shared roots has not been explicitly addressed so far.


Anchor accuracy of new intra-language links could not be calculated because of the unavailability of gold standard data.

the proposed method specifies the anchor for destination article only when possible anchors for it is found in the target-language article .


Both methods and our proposed method exploit the comparability between intra-language links in different language editions.

while the former find new ILLs, the latter finds new intra-language links.


Collected messages are dated from Jun 21, 2008 to Nov 7, 2009, and all of them are in Traditional Chinese.

the literal meanings of the posted messages need to be known.


We argue that these patterns are more static than the others, and we call them the customary patterns.

the patterns in Sections 4.1, 4.2 and 4.3 are called noncustomary patterns.


Since the Plurk platform can be used as an instant messaging system, and readers of the message are usually on the author's friend list, these messages are usually conversational.

ya-hoo blogs are not limited in length and a blog article itself is not part of the conversation.


For example, "太強" (so strong) is listed as a positive term in NTUSD.

it is used to indicate a negative condition in the example (s7).


Furthermore, several annotation efforts have been devoted to developing resources for different languages, needed for supervised learning (Hajič et al., 2009).

there is still a large number of languages for which corpora with semantic annotations do not exist.


The ideas behind their cross-lingual model adaptation resemble the ideas behind our global method for semantic role labelling.

in contrast to their work we do not consider the predicate labelling as given because, as manual annotations show (van der Plas et al., 2010), this task is not trivial.


This strong correlation between syntactic labels and semantic role labels in the PropBank annotation has been shown in detail by Merlo and Van der Plas (2009).

to previous work on monolingual unsupervised semantic role induction, we add the predicate label as a predictor.


We show that the combination of direct transfer (a high-precision method) and global methods (high in recall) outperforms previous results.

to previous work, we transfer predicate annotations and semantic role annotations by building two separate models tailored to the task at hand.


One approach for building such a multilingual semantic parsing system is to develop a joint generative process from which both the semantic representations and the sentences in different languages are generated simultaneously.

building such a joint model is non-trivial.


We can see from the results presented in Table 3 and Table 4 that, in general, the performance of the multilingual semantic parser tends to improve as the number of input languages increases.


der words which are essential for reliable clustering of the first order words.

the large number of occurences and a large vocabulary make it intractable to run LDA using the original frequency of the second order words.


In previous self-training algorithms, the learner tries to convert the most confidently predicted unlabeled examples of each class into labeled training examples.

they evaluate the confidence of an instance only based on the individual evidence from the instance.


The straightforward way is to evaluate each instance with their linked confidence P L () from the classifier.

it oversimplifies the data dependence and does not make use of the correlated characteristics.


This conclusion for SA was opposite to TTC, so tp was preferred in subsequent SA research.

to local weight, global weight depends on the whole document collection.


The only difference between them lies in the quantification of the imbalance of a term's distribution.

existing methods more or less suffer from the problem of overweighting.


However, labeling reviews is often difficult, expensive or time consuming (Chapelle et al., 2006).

it is much easier to obtain a large number of unlabeled reviews, such as the growing availability and popularity of online review sites and personal blogs (Pang and Lee, 2008).


There are several works have been done in semi-supervised learning for sentiment classification, and get competitive performance (Li et al., 2010;Dasgupta and Ng, 2009;.

most of the existing semi-supervised learning methods are still far from satisfactory.


The model does not suffer from the label-bias problem as does the Maximum Entropy model, and its parameter estimation is well behaved with the help of a convex loss function.

the convexity of the loss function of the CRF model does not hold anymore when there are unobserved data or latent variables (Sutton and McCallum, 2007).


2011proposed eight rules to describe these relations.

their work only focused on English sentences, whereas the relations for Chinese sentences are different.


One of the main differences between a sentiment sentence and a formal sentence is that the former often contains polarity words.

to the features of feeling(•), polarity words (e.g., "great" in the sentence "Overall, this is a great camera") tend to be retained, because they are important and special to sentiment analysis.


Moreover, we can observe that the idea of sentence compression and our Sent Comp are useful for all the four product domains on T-P collocation extraction task, indicating that Sent Comp is domain adaptive.

we can find a small gap between auto Comp and manual Comp, which indicates that the Sent Comp model can still be improved further.


By an additional interpretation of the templates of these yield functions in the algebra of dependency trees (with the overt lexical items as roots), the LCFRS generates both strings and (possibly non-projective) dependency structures.

the running time of LCFRS parsers is generally very high, still polynomial in the sentence length, but with a degree determined by properties of the grammar; difficulties involved in running LCFRS parsers for natural languages are described by (Kallmeyer and Maier, 2013).


c B(c B(a A( ) b) d) d. We couple derivations in two grammars in a way similar to how this is commonly done for synchronous grammars, namely by indexed symbols.

we apply the mechanism not only to derivational nonterminals but also to terminals.


The highest score from the top six relations is achieved by taking words exclusively from the second-order secondary object (OBJ2) relation.

relatively few word types are included in the clusters.


numerals and determiners were simply parts of an NP but had no distinct labeling, like [NP azöt [ADJP fekete] kutya] (the five black dog) "the five black dogs"), but it was necessary to assign them a dependency label and a parent node during conversion.

in some cases it was not straightforward which modifier modifies which parent node: for instance, in [NP nem [ADJP megfelelő] módszerek] (not appropriate methods) "inappropriate methods", the negation word nem is erroneously attached to the noun instead of the adjective in the converted phrase.


For instance, coordination and multiple modifiers are among the most frequent sources of errors in both cases as for the error rates are concerned.

with regard to the absolute numbers, we can see that both error types are reduced when the gold standard dataset is used for training.


In formal terms that we need for the outline of the transduction below, a SSyntS is defined as follows: Definition 1 (SSyntS) An SSyntS of a language L is a quintuple T SS = N, A, λ ls→n , ρ rs→a , γ n→g defined over all lexical items L of L, the set of syntactic grammemes G synt , and the set of grammatical functions R gr , where • the set N of nodes and the set A of directed arcs form a connected tree, • λ ls→n assigns to each n ∈ N an l s ∈ L, • ρ rs→a assigns to each a ∈ A an r ∈ R gr , and The features of the node labels in DSyntSs as worked with in this paper are lex dsynt and "semantic grammemes" of the value of lex dsynt , i.e., number and determination for nouns and tense, aspect, mood and voice for verbs.

2 to lex ssynt in SSyntS, DSyntS's lex dsynt can be any full, but not a functional lexeme.


If required, a SSyntS-DSyntS structure pair can be also mapped to a pure predicate-argument graph such as the DELPH-IN structure (Oepen, 2002) or to an approximation thereof (as the Enju conversion (Miyao, 2006), which keeps functional nodes), to an DRS (Kamp and Reyle, 1993), or to a PropBank structure.

dSyntS-treebanks can be used for automatic extraction of deep grammars.


From WordNet (Fellbaum, 1998;Bond et al., 2009), we can derive entailment and contradiction relations using synsets and synset-links that represent relations such as 'troponym', 'antonym' and 'entailment'.

happens-before and anomalous obstruction relations cannot be derived from it, since there is no information on temporal ordering except that on causality.


In comparison to the n-gram baselines, only the parser by Seginer yields a higher score for frequent words and 1M sentences training in Setup A.

the difference is very small and is confirmed on the 10M sentences only in comparison to the Trigram baseline.


This means that for those particular tasks, disambiguation plays an important role.

this is not the case for the noun-noun pairs.


We introduce this hypothesis producer because the baseline system tends to miss long edit regions, especially when very few words in the region are repeated.

sometimes a speaker does change what he intends to say by aborting a sentence so that only the beginning few words are repeated, as in the above sentence.


Our preliminary experiments show that by increasing the clique order of features while reducing the number of labels (keeping about the same total number of parameters), we can maintain the same performance.


This does not solve the problem entirely, since we know that named entities are not the only interesting nuggets -general terms and concepts can also be of interest to a reader.

we do have reason to believe that entities play a very prominent role in web content consumption, based on the frequency with which entities are searched for (see, for example Lin et al.


CDCD can be seen as a sub-task in the emerging wider field of argumentation mining that involves identifying argumentative structures within a document, as well as their potential relations (Mochales Palau and Moens, 2009;Cabrio and Villata, 2012;Wyner et al., 2012).

cDcD has several distinctive key features.


However, since CDCs often correspond to much smaller parts of their surrounding sentence, considering the scores of all previous components is more effective.

to the components described above, for which the training set is fully defined by the labeled data, the Ranking Component needs be trained also on the output of its "upstream" components, since it relies on the scores produced by these components.


Thus, it is possible to derive argumentative relations between components though they are not explicitly included.

to our work, the corpus consists of several text genres including newspaper editorials, parliamentary records, judicial summaries and discussion boards.


Therefore, they annotate a pair of arguments as either entailment or not.

to our work, the approach models relationships between pairs of arguments and does not consider components of individual arguments.


However, the approach is tailored to product reviews, and the work does not provide an inter-rater agreement study.

to previous work, our annotation scheme includes argument components and argumentative relations.


In this example, both components cover a complete sentence.

a sentence can also contain several argument components like in example (5).


Parallel treebanks are not something new.

most of the existing parallel treebanks (Li et al., 2012;Megyesi et al., 2010) do not have phrase alignments.


The holder of the epistemic modality in example 6 is not the Twitter user, either.

the Twitter user is not quoting anyone here, but is rather making an assumption about what the Egyptian National Party holds as TRUE.


Edges are not drawn between different nodes for the same mention.

they are drawn between two entities when there is a relation between them.


Experiments show that overall using the R m combining scheme is better than the R s scheme.

the highest rank, after combining graph rank score and initial confidence score, is not always correct.


That explains the little improvement over basic PR when using the  initial confidence as an initial rank before using PR (see Table 1).

when comparing PR results in Tables 2 and 1, we can see that the PR algorithm is more sensitive to the links than to initial ranks.


Straightforward cases like these were used to justify the simple aggregation methods used by all TSF systems to date (Surdeanu, 2013;.

in reality even humans often must deal with vague and/or conflicting temporal information across documents, and systems must furthermore deal with the fact that each of their temporal relationship classifications is potentially false.


Each RNP holds at the DCT, and "Wednesday", as well as the day before that (the VTOP of "pardoned").

as for VTOP's further into the past, whether the post-relational state holds is less clear.


In addition, previous truth finding work assumed most claims are likely to be true.

most SF systems have hit a performance ceiling of 35% F-measure, and false responses constitute the majority class (72.02%) due to the imperfect algorithms as well as the inconsistencies of information sources.


We have discussed semantic spaces of relation expressions and the common semantic space as if to define what constitutes a relation expression is straightforward.

it is not trivial to define what constitutes a relation expression.


One can easily see that the common space successfully moved down many ambiguous expressions such as {compra} and {nor} in {announce acquisition}, and {would say} and{,} in {president ,}.

some relation expressions which are specific and semantically similar to the chosen ones moved up in the rank, for example {'s purchase} and {chief ,}.


This approach got the lowest results during the SemEval-2013 Task 12 evaluation due to a bug in the system.

the correct implementation achieves 0.583 of F-measure for English and 0.528 for Italian.


Previous WSI evaluations in SemEval (Agirre and Soroa, 2007;Manandhar et al., 2010) have approached sense induction in terms of finding the single most salient sense of a target word given its context.

as shown in Erk and McCarthy (2009), multiple senses of the target word may be perceived by readers from different angles and a graded notion of sense labeling may be considered as the most appropriate.


Due to the unsupervised nature of the task, participants were not provided with sense-labeled training data.

wSI systems were provided with the ukwac corpus (Baroni et al., 2009) to use in inducing senses.


and finance corpora (Koeling et al., 2005) and the BNC.

in these experiments we observed very high Novelty Ratio for many distractors (selected in a similar way to our other experiments).


The generalisation error is given by and approximated by its empirical counterpart on a finite m-sample of pairs where G i is a word graph and p i the best summarising sentence.

minimising the empirical risk directly leads to an ill-posed optimisation problem as there generally exist many indistinguishable but equally well solutions realising an empirical loss of zero.


For small training sets, the structural support vector machine performs only slightly better than the unweighted application of Yen's algorithm and is clearly outperformed by the unsupervised baselines.

the SVM improves by a generalised, loss-augmented shortest path algorithm that can be solved by an integer linear program in polynomial time.


This deficiency is problematic because in practical usage the maximum length of a summary is specified by the user; hence, the summarizer should be able to control output length.

to their method, our approach naturally takes the maximum summary length into account when summarizing a document.


The words in the document leads were likely to be important, and LEAD drew on this property.

as we mentioned later, it sacrificed the linguistic quality to achieve the high ROUGE score.


At first glance, we can mitigate this problem using distant supervision approaches.

there is difficulty in applying these approaches to MR classification: only one of the relation types defined in the 2010 i2b2 Challenge is represented in the Unified Medical Language System 1 , the most comprehensive medical ontology available to date.


whereĈ t+1 alt represents the estimated CPD at t + 1.Ĉ t alt represents the estimated CPD at t. The other variables are the same as those in Equation (2).

we assume that the system can observe G user and S alt .


G user is not usually observable because traditional dialogue systems have automatic speech recognition/Spoken language understanding errors.

in this work, we use Wizard of Oz in place of automatic speech recognition/Spoken language understanding (Section 6.2).


Particularly, both of the learned policies better achieve user satisfaction than Random.

only Framing is able to achieve better persuasion success than Random.


Note that in the previous section, Sat user and P S sys are estimated from the simulated dialogue.

to the previous section, Sat user and P S sys are calculated from the result of the real user's questionnaire  But, A has extremely good performance.


While there is one previous example of persuasive dialogue using framing (Mazzotta et al., 2007), this system does not use an automatically learned policy, relying on handcrafted rules.

in our research, we apply reinforcement learning to learn the system policy automatically.


Language, as the primary form of human expression, is certainly critical.

analyzing meaning may require going beyond linguistic inference, depending on the context or application.


Frequently, their meanings are also systematically related.

there are also many examples of derivationally related lemma pairs whose meanings differ substantially, e.g., object N-objective N. Most broad-coverage derivational lexicons do not reflect this distinction, mixing up semantically related and unrelated word pairs.


There are almost no compound errors C, which is not surprising given the rule-based construction of the lexicon, and only a relatively small number (about 5%) of lemmatization errors L, which fall outside the scope of our work.

both N and M occur with substantial frequency: Each class accounts for around 10% of the pairs.


The advantage of the string transformation-based construction of DERIVBASE is its ability to include infrequent lemmas in the lexicon, and in fact DERIVBASE includes more than 250,000 content lemmas, some of which occur not more than three times in SDeWaC.

this is a potential problem when we build distributional representations for all lemmas in DERIVBASE since it is known from the literature that similarity predictions for infrequent lemmas are often unreliable (Bullinaria and Levy, 2007).


For example, classic dance is annotated as correct out of context because one could imagine using it in a context where it would denote some typical dance like: (13) They performed a classic Ceilidh dance.

in practice, the AN classical dance is used much more frequently, and classic dance is most often errorful in context, as in (4) above.


Since the ANs in these pairs are semantically similar, the features based on their semantic representations might not be discriminative enough.

the classifier is more effective in detecting errors in cases where the original AN and its correction are only similar in form, or not related to each other.


If we can generate sufficiently appropriate rules, these approaches seem to be effective.

there are many types of derivational patterns in SNS text and it is difficult to cover all of them by hand.


This enables our system to perform well even when the number of candidates increases.

several studies have applied a statistical approach.


In these studies it was assumed that clear word segmentations existed.

since Japanese is an unsegmented language the normalization problem needs to be treated as a joint normalization, word segmentation, and POS tagging problem.


Example Here is an example of a tweet that contains a URL: (1) #Localization #job: Supplier / Project Manager -Localisation Vendor -NY, NY, United States http://bit.ly/16KigBg #nlppeople The words in the tweet are all common words, but they occur without linguistic context that could help a tagging model to infer whether these words are nouns, verbs, named entities, etc.

on the website that the tweet refers to, all of these words occur in context: (2) The Supplier/Project Manager performs the selection and maintenance .


First of all, naive self-training does not work: accuracy declines or is just around baseline performance (Table 2 and Figure 3).

our augmented self-training methods with WEB or DICT reach large improvements.


Unsupervised classbased language models such as Random Forest LM (Xu and Jelinek, 2007), Model M (Chen, 2008) have been investigated that outperform a word-based LM.

the long-distance information is captured by using a cache-based LM that takes advantage of the fact that a word observed earlier in a document could occur again.


From  is statistically significant to the class-based LM (Brown et al., 1992) and DCLM (Chien and Chueh, 2011) at a significance level of 0.01 and 0.05 respectively.

the IDCLM (L = 3) model is statistically significant to the above models at a significance level of 0.01.


Regarding different classes of uncertainty, we should mention that while weasels constitute the most frequent cue category in Wikipedia texts, they occur less frequently in the news corpus.

doxastic cues are frequent in the news corpus but in Wikipedia texts, their number is considerably smaller.


One possibility would be to use a specific label from the existing set of dependency relations, for example 'mwe'.

one-to-many alignments do not always refer to proper multi-word expressions but often represent other grammatical or structural differences like the relation between the English preposition 'of' which is linked together with the determiner 'the' to the German determiner 'der' in sentences like 'Resumption OF THE session' translated to German 'Wiederaufnahme DER Sitzung'.


Somewhat surprisingly we can see that the recall-oriented alignment heuristics (grow-diag-final-and) actually perform quite well in many cases, leading either to the best performing model or to one that is very close to the best result.

in some cases, these models fall behind the ones based on alignment intersections (for instance Spanish-English) or directional word alignments (for example for Spanish-German, French-English, Swedish-German).


Because of integrating out all Gs in all of the priors, interdependency between samples of α ij,pq |B ij,pq = b or β ij,pq |B ij,pq = b is introduced, resulting in simultaneously obtaining multiple samples impractical.

blocked sampling, which obtains sentence-level samples simultaneously Johnson et al., 2007a) is attractive for the fast mixing speed and the easy application of standard dynamic programming algorithms.


Human-defined word class knowledge is useful to address this issue.

the manual word class taxonomy may be unreliable and irrational for statistical natural language processing, aside from its insufficient linguistic phenomena coverage and domain adaptivity.


It takes the empirical rules and probabilities from a Treebank.

due to the context-free assumption, PCFG does not always perform well (Klein and Manning, 2003).


On one hand, this procedure provides a more rational hierarchical subcategorization structure according to data distribution.

the order of the division criterions represents the priorities the grammar induction takes for each criterion.


It would be promising if this work stacks with the content word knowledge.

the work with content word knowledge have to handle the polysemous words in the semantic taxonomy, so they split the categories according to the data, and then find a way to map the subcategories to the node in the taxonomy, and constrain their further splitting.


For example, given a phrase pair ⟨"yingyun", "business"⟩, its orientation is more likely to be monotone if it is preceded by a noun phrase pair such as ⟨"xinyongka", "credit card"⟩.

the probability of the discontinuous orientation is higher if the previous phrase pairs contain verbs such as ⟨"gaishan", "improve"⟩.


Note that some words may not be aligned correctly, like "NULL-musiciens".

generating these tuples can be viewed as a language model process that exploits previous source and target words, and current source word contained in previous tuples like "les-musiciens".


For example, there exists slight difference between the word-bilingual-tuple-I and the word-bilingual-tuple-II for the English-French task.

for the English-German task, the word-bilingual-tuple-II significantly outperforms the word-bilingual-tuple-I by 0.4 BLEU scores.


Somewhat surprisingly, this is not the case.

both 5-and 7-gram class-based models perform slightly worse than the stream-based models.


The dependence of translation choice on domain suggests that the word alignments themselves can better be conditioned on domain information.

in the data selection setting, corpus C mix often does not contain useful domain markers, and C in contains only a small sample of in-domain sentence pairs.


Their estimate during training might be a reasonable selection cut-off threshold.

we found that it is not entirely clear whether these cut-off criteria might exclude other relevant/irrelevant pairs that are not exactly in-domain.


This work looks at modeling the relevance of sentence pairs from the mix-domain corpus to a task represented by an in-domain sample.

with previous work we cast this as a translation problem with a latent domain variable.


There is a growing interest in automatically predicting the gender and age of authors from texts.

most research so far ignores that language use is related to the social identity of speakers, which may be different from their biological identity.


Most of the studies on language and age focus on chronological age.

speakers with the same chronological age can have very different positions in society, resulting in variation in language use.


We observe that in the Wikipedia part of the Lassy Large corpus, modal clusters occur in the 1-2 order only 0.5% of the time.


This process avoids subjectivity beyond the definition of the construction.

it limits the variables that can be used in the study to the ones that are, or can be, automatically annotated in a corpus.


Clearly the model predicts better than that.

it should be noted that this is not a typical predictive task.


In section 4 we discussed our choice for the Wikipedia part of the Lassy Large corpus.

this corpus consists of other kinds of sources as well, and now that we have a highly automated way of building the model, it is relatively easy to test it on a different part of the corpus to see whether the same variables hold in a different domain of text.


While extractive techniques are generally preferred over abstractive ones (as abstraction can introduce disfluency), existing extractive summarizers are either supervised or based on heuristics of certain desired characteristics of the summarization result (e.g., maximize n-gram coverage (Nenkova and Vanderwende, 2005), etc.).

when it comes to online reviews, there are problems with both approaches: the first one requires manual annotation and is thus less generalizable; the second one might not capture the salient information in reviews from different domains (camera reviews vs. movie reviews), because the heuristics are designed for traditional genres (e.g., news articles) while the utility of reviews might vary with the review domain.


While there is a large overlap between text summarization and review opinion mining, most work focuses on sentiment-oriented aspect extraction and the output is usually a set of topics words plus their representative text units (Hu and Liu, 2004;Zhuang et al., 2006).

such a topic-based summarization framework is beyond the focus of our work, as we aim to adapt traditional extractive techniques to the review domain by introducing review helpfulness ratings as guidance.


The idea of using sLDA in text summarization is not new.

the model is previously applied at the sentence level (Li and Li, 2012), which requires human annotation on the sentence importance.


Text simplification has often been defined as the process of reducing the grammatical and lexical complexity of a text, while still retaining the original information content and meaning.

text can also be simplified in other ways; for instance, by removing peripheral information to reduce text length, through sentence compression or summarisation.


Out of 50 participants, 3 rated themselves as native.

they could get only about 28% of the answers correct, showing the fact that the participants had overestimated themselves.


Her choice of properties may be sufficient to distinguish it from all its distractors in the current context.

unlike (1), (2) is a composite utterance consisting of two communicative modalities, each of which contributes to the communicative intention (Enfield, 2009).


This combination is closely matched for accuracy by the combination involving descriptive features, Distance and DistProps.

dropping Distance (using only descriptive features and DistProps) results in worse performance.


Adding only DistProps to the descriptive features improved the accuracy of the Logistic classifier somewhat, though it had a greater impact on J48.

distance seems to have the greatest impact of the two physical features.


This combination also exceeds the combination of descriptives, DistProps and Distance, though only marginally.

this does remain the next best combination for J48, consistent with the results on the complete dataset.


The experiments show that many acrostics based on common English words can be generated in less than a minute.

we see our main contribution in the presented technology paradigm: a novel and promising combination of methods from Natural Language Processing and Artificial Intelligence.


Schwarzenegger himself characterized the appearance of that acrostic a "wild coincidence".

1 such a coincidence is highly unlikely: Using the simplistic assumption that first letters of words are independent of each other if more than ten words are in between (line length in the Schwarzenegger letter) and calculating with the relative frequencies of first letters in the British National Corpus (Aston and Burnard, 1998), the probability for the acrostic in Figure 1 can be estimated at 1.15 • 10 −12 .


Other early methods use machine translation (Quirk et al., 2004).

the need for large and expensive parallel manual translation corpora cannot be circumvented by using multiple resources (Zhao et al., 2008).


Still, the rich variety of the full data set can form a semantically strong operator.

in our pilot experiments, the full PPDB patterns often decreased text quality unacceptably, such that we refrained to use PPDB as a single operator in our experiments.


Therefore, it is possible for translation retrieval to have access to a huge volume of monolingual corpora that are readily available on the Web.

the MT + IR pipeline suffers from the translation error propagation problem.


In Figure 1, the search graph has 9 nodes, 10 edges, 4 paths, and 3 distinct translations.

the translation option graph has 6 nodes, 9 edges, 10 paths, and 10 distinct translations.


Now that we have explored the role of syntax in this project, our next step is try to further improve our QE system by adding semantic information.

there are many other ways in which the research in this paper could be further extended.


For example, r 8 in Figure 1 has a good Chinese syntactic structure indicating the reordered translations of NP and VP.

such a rule would not normally be included in a Hiero grammar, as it would require consecutive source language non-terminals (see Figure 3).


We see that left-heavy binarization is very helpful and exp08 achieves overall improvements of 1.2 and 0.8 BLEU points on the newsire and web data.

right-heavy binarization does not yield promising performance.


However, all these methods still resort to rule extraction procedures similar to that of the standard phrase/hierarchical rule extraction method.

we use the GHKM method which is a mature technique to extract rules from tree-string pairs but does not impose those Hiero-style constraints on rule extraction.


In general, such a list could contain hundreds or even thousands of entities.

the mention should be linked to at most one entity in the list.